class_docstr: Text Processor class implement NER, POS tag, lexical tree parsing.
  EnProcessor is designed by single instance mode.
docstr: Split text to sentences.
  
  :param str text: text string
  :return: list[str]
docstr_tok: ["Split","text","to","sentences",".",":","param","str","text",":","text","string",":","return",":","list","[","str","]"]
code: def sentence_tokenize(self, text):
      r"""
      Split text to sentences.
  
      :param str text: text string
      :return: list[str]
  
      """
      assert isinstance(text, str)
      self.check_initialized()
      text = self.nlp.tokenizer(text)
  
      if not self.__sent_tokenizer:
          if "sentencizer" not in [
              pipeline[0] for pipeline in self.nlp.pipeline
          ]:
              self.nlp.add_pipe("sentencizer")
          self.__sent_tokenizer = self.nlp.pipeline[-1][1]
  
      return [sent.text for sent in self.__sent_tokenizer(text).sents]
  
imports: import re
  import nltk
  import spacy
  import threading
  from functools import reduce
  from spacy.tokens import Doc
  from .nltk_res_load import *
  from ..settings import *
label_desc: Use image node_id 5 for calling the EnProcessor obj's underlying member method code with example usage: obj.sentence_tokenize(text) and returns: unknown